pandas.core.window.Expanding.var

Expanding.var(ddof=1, *args, **kwargs) [source]

Calculate unbiased expanding variance.

Normalized by N-1 by default. This can be changed using the ddof argument.

Parameters:	ddof : int, default 1 Delta Degrees of Freedom. The divisor used in calculations is N - ddof, where N represents the number of elements. *args, **kwargs For NumPy compatibility. No additional arguments are used.
Returns:	Series or DataFrame Returns the same object type as the caller of the expanding calculation.

See also

Series.expanding

Calling object with Series data

DataFrame.expanding

Calling object with DataFrames

Series.var

Equivalent method for Series

DataFrame.var

Equivalent method for DataFrame

numpy.var

Equivalent method for Numpy array

Notes

The default ddof of 1 used in Series.var() is different than the default ddof of 0 in numpy.var().

A minimum of 1 period is required for the rolling calculation.

Examples

>>> s = pd.Series([5, 5, 6, 7, 5, 5, 5])
>>> s.rolling(3).var()
0         NaN
1         NaN
2    0.333333
3    1.000000
4    1.000000
5    1.333333
6    0.000000
dtype: float64

>>> s.expanding(3).var()
0         NaN
1         NaN
2    0.333333
3    0.916667
4    0.800000
5    0.700000
6    0.619048
dtype: float64